Throughout history, the primary means of transportation for humans has been on foot. We present a software tool which can help visualize and predict where historical trails might lie through the use of a human-centered cost metric, with an emphasis on the ability to generate paths which traverse several thousand kilometers. To accomplish this, various graph simplification and path approximation algorithms are explored. We show that it is possible to restrict the search space for a path finding algorithm while not sacrificing accuracy. Combined with a multi-threaded variant of Dijkstra’s shortest path algorithm, we present a tool capable of computing a path of least caloric cost across the contiguous US, a dataset containing over 19 billion datapoints, in under three hours on a 2.5 Ghz dual core processor. The potential archaeological and historical applications are demonstrated on several examples.